Agreeing Not to Disagree: Iterative Versus Episodic Forms of Political Participatory Behaviors

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Copyright © 2016 (Yangsun Hong & Hernando Rojas). Licensed under the Creative Commons Attribution Non-commercial No Derivatives (by-nc-nd). Available at http://ijoc.org.

Agreeing Not to Disagree:

Iterative Versus Episodic Forms of Political Participatory Behaviors

YANGSUN HONG

HERNANDO ROJAS

University of Wisconsin–Madison, USA

People talk about politics with others who may or may not share their views. These conversations shape their understanding and engagement with politics. However, studies have resulted in a conundrum in the relationship between disagreeable discussion and participation. Some studies suggest that the relationship is likely contingent on the type of participation. In addition, considering the characteristics of one’s social networks alongside exposure to disagreement serves to extend our understanding of how communication matters for political engagement. Our results suggest that episodic forms of participation, such as voting or protesting, are not directly impacted by exposure to disagreement, whereas iterative forms, including certain forms of civic engagement and expressive behaviors, are enhanced by exposure to political disagreement, particularly among those with larger discussion networks.

Keywords: political participation, political disagreement, conversation networks

Interpersonal political conversation has received a lot of attention from scholars because it is an important mechanism for promoting democratic citizenship (Delli Carpini, Cook, & Jacobs, 2004; Habermas, 1989), so much so, in fact, that some scholars argue that “talk-centric democratic theory” has replaced “voting-centric democratic theory” (Chambers, 2003). These scholars highlight the importance of communication processes as integral drivers of participatory behavior (Chambers, 2003). Everyday, interpersonal political conversations benefit society by enhancing political knowledge (Eveland, 2004), cognitive complexity about politics (McLeod, Scheufele, Moy, Horowitz, et al., 1999), political identity (Walsh, 2003), political efficacy (Rojas, 2008), and community engagement (Kwak, Williams, Wang, & Lee, 2005).

In particular, scholars have identified disagreement in everyday political talk as especially important for informed decision making (Cappella, Price, & Nir, 2002). However, empirical research has produced contradictory results about the relationship between exposure to disagreement and participation. Some studies have shown that disagreement negatively affects civic and political participation because it increases ambivalence and social accountability (McClurg, 2006a, 2006b; Mutz, 2002, 2006). In other words, exposure to divergent viewpoints via crosscutting networks discourages

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turnout, delays vote choice, reduces interest in politics, and fosters ambivalence (Berelson, Lazarsfeld, & McPhee, 1954; Mutz, 2006). On the other hand, other studies have shown that frequent discussion within heterogeneous networks and exposure to disagreement increase civic and political participation (Leighley, 1990; Rojas, 2008), mostly as an outcome of increased political knowledge that results from such encounters (Eveland, 2004).

To reconcile these differences across studies, one could think of at least two explanations. One hinges on the definition of disagreement itself. Although numerous studies have been published in the area, scholars have not yet reached an agreement about how to understand disagreement. Whereas a common theme of disagreement is that of individual exposure to different opinions on issues, others consider more severe types such as conflict and incivility (Richmond & McCroskey, 1979; Teven, McCroskey, & Richmond, 1998). Another possibility is to consider whether different types of participation might be involved, which was the focus of our study.

Recent studies suggest that distinguishing the types of participation would shed light on this controversy, and argue that the relationship between disagreement and participation is contingent on participation type (Lee, 2012; Pattie & Johnston, 2009). But the lack of consensus in the academic community on how to distinguish among various forms of participation adds to, rather than clarifies, the confusion resulting from contradictory results. This study aimed to contribute to the literature on disagreement and participation, taking into account structural network characteristics. It analyzed whether disagreeable political talk has different influences on various types of participation, and whether these effects are amplified or muted by the size of people’s social networks.

Political Discussion

A number of studies provide evidence that social engagement, including memberships in civic groups, churches, and workplaces, is positively associated with political participation (Leighley, 1996; Putnam, 2000). Scholars have explained that this relationship is largely a result of political conversations that people have in these settings. Political conversation provides opportunities to learn civic skills, stimulates civic spirit and volunteerism, increases the likelihood of becoming a target of political recruitment, and develops collective interest in politics (Putnam, 2000).

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H1: Network size will be positively related to political participation, in general.

Although individuals certainly make choices about their discussion networks, it is important to keep in mind that these choices are constrained by social structure (Blau, 1977; Huckfeldt & Sprague, 1987; Knoke, 1990). An individual can choose discussants within his or her network, but these choices are mostly possible only within a larger social context, including the workplace, place of residence, and community at large. Some of these contexts offer differing possibilities for these interactions to take place among people who are more similar or dissimilar to one another. For example, employment outside the home tends to favor interactions with a broader range of people and with differing levels of intensity. In the social network literature, these different types of interactions are typically captured using measures of tie strength and social homophily.

Tie strength refers to the depth of the social bond and has been conceptualized in terms of the amount of time, emotional intensity, intimacy, and reciprocity among the parties involved (Granovetter, 1973). It has been commonly operationalized as weak versus strong ties (Granovetter, 1973). Strong ties typically share similar backgrounds, including class, race, religion, education, and political ideology, whereas weak ties tend to hold more divergent backgrounds. Homophily, on the other hand, refers to the similarity of the people immersed in an interaction.

Although tie strength and homophily are conceptually distinct, there is some overlap between these concepts, with strong ties tending toward homophily (Coleman, 1957; McPherson, Smith-Lovin, & Cook, 2001) and weak ties toward heterophily. The underlying idea is that the commitment a strong tie requires is either based on existing similarities or that similarity is achieved over time (Granovetter, 1973). However, and this is critical for our argument, the number of strong ties a person can have is usually low because of the costs associated with maintaining such relationships. Although there are some discrepancies in operationalizing these concepts in the literature, there are striking similarities across countries and cultures regarding the number of strong ties in a personal network that hover around six worldwide (Wellman, 1988).

Granted, the number of core discussants within this strong tie network can be even smaller (see, e.g., Hampton, Sessions, & Her, 2011), but for our purposes the significant factor is that as the size of the discussion network increases, out of necessity, the higher the probability that a person has more weak ties in his or her discussion networks (Granovetter, 1973). Thus, the chances of being exposed to dissimilar views increases with discussion network size (Huckfeldt, Beck, Dalton, & Levine, 1995; Rojas, 2008).

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Disagreement in Political Discussion

Is interpersonal discussion with others who have different points of view beneficial or harmful for democratic citizenship? To answer this question, it is useful to trace back to Habermas’ (1989) work on the public sphere and some of the criteria for an ideal public sphere: First, as many citizens as possible need to join the political procedures and discourses about social issues; second, the quality of the discourse depends on its rationality that is ultimately established through intersubjective agreement. Following this logic, discussions with people who have dissimilar viewpoints play a critical role in the functioning of a public sphere or spheres. This approach has long been upheld by political theorists (e.g., Arendt, 1961; Barber, 1984; Fishkin, 1991; Guttmann & Thompson, 1996), and there is empirical evidence that supports these normative claims. However, scholarship also has pointed out that despite the cognitive gains that are made possible by political discussion across differing viewpoints, these crosscutting exchanges may have a demobilizing effect that is not necessarily beneficial for democracy.

Scholars studying the impact of political disagreement on participatory activities can be grouped into three different camps. One line of study found that exposure to disagreement negatively affects democratic citizenship because of “cross-cutting pressures” (Berelson et al., 1954; Mutz, 2002, 2006). Mutz (2002) proposed that individuals surrounded by others who have different points of view tend to have higher levels of ambivalence and try to avoid controversy. Consequently, exposure to disagreement would promote political apathy rather than political engagement and lead to indecision or social withdrawal (Mutz, 2002). Moreover, individuals facing disagreement can feel the pressure of being accountable about their own views when disagreeing with others, and this psychological burden may reduce their willingness to express views and or take political actions (Mutz, 2006).

Another group of scholars contends that encountering disagreement positively relates with political activities (McLeod, Scheufele, Moy, Horowitz, et al., 1999; Scheufele, Nisbet, Brossard, & Nisbet, 2004) and civic activities (Rojas, 2008). This line of research argues that interactions with others having dissimilar views help to increase understanding and comprehension of their oppositional views, providing an opportunity to rethink and refine one’s own viewpoints. Within this logic, encountering disagreement serves as a vehicle for deliberation and results in increased participation as one clarifies ideas, as well as the ideas of others. There have been a number of studies showing evidence of the positive associations between political disagreement and political knowledge (McLeod, Scheufele, & Moy, 1999), political tolerance (Mutz, 2006), awareness of rationales for one’s own and others’ political views (Cappella et al., 2002; Mutz, 2006), political engagement (Mutz, 2006), willingness to participate in public forums (McLeod, Scheufele, Moy, Horowitz, et al., 1999; Moy & Gastil, 2006), voting (McLeod & Lee, 2012), and participation in campaign activities (McClurg, 2006a, 2006b; Pattie & Johnston, 2009).

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political participation considered across studies (Lee, 2012; Pattie & Johnston, 2009). Importantly, studies finding a negative association between exposure to disagreement in conversation and political participation tended to focus on electoral-related or episodic-types of participations such as voting (McClurg, 2006a; Mutz, 2002), whereas studies finding a positive association were more likely to consider ongoing or iterative types of activities such as volunteering (Pattie & Johnston, 2009; Wojcieszak, Baek, & Delli Carpini, 2010). This study sought to contribute to this controversy by formally contrasting different forms of participation in relation to exposure to disagreement in political conversation.

Types of Participatory Activities

Scholars have investigated differences in political participation based on various criteria. For example, Scheufele and Eveland (2001) categorize political participation into “public” participatory behaviors, such as expression of one’s own opinion, versus “nonpublic” participatory behaviors, such as voting, and argue that exposure to disagreement in discussion tends to decrease one’s willingness to participate in “public” participatory activities. The experience of disagreement in political discussion would make people believe that they will need to be more accountable about their viewpoints, particularly when engaged in public activities, which are more easily seen or noticed by other people. According to this distinction, social accountability would be a smaller issue for nonpublic participation, but would matter for public participatory activities.

Pattie and colleagues (Pattie & Johnston, 2009; Pattie, Seyd, & Whiteley, 2004) distinguish among forms of political participation using a resource-based categorization scheme (Verba, Schlozman, & Brady, 1995). They attempt to differentiate participation based on two dimensions: cost (low cost vs. high cost) and cooperation (privatized vs. collective). Based on this logic, Pattie and colleagues (2004) suggest three distinct types of participation: individual acts (voting, signing a petition), contact participation (sending letters to government, attending governmental meetings), and collective participation (attending rallies and protest meetings). They found that the negative association between exposure to disagreement in discussion and participation was stronger in low-cost participatory activities because people tended to have a smaller stake in the outcome and, as a result, they did not have enough strong reasons to ignore or overcome the effects of disagreement. In other words, the level of someone’s commitment turns out to be an important determinant of how people process the political disagreement encountered while having political conversation with others. In a subsequent study, Pattie and Johnston (2009) found that exposure to disagreement in discussion was negatively associated with electoral-related participatory activities, but positively related to other types of activities. Thus, the authors expressed an optimistic view of the relationship between exposure to disagreement and political participation.

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participants. Thus, position-taking participation requires the participants to choose either to join the activity to support their issue stance or not, such as signing a petition, voting, and protesting. On the other hand, non–position-taking activities refer to the “activities not designed for people to express support for a fixed position” (p. 546). Participants have opportunities to express the complexity of their thoughts and ambivalence in feelings about political issues during their participation in the activity. These would include behaviors such as writing a letter to a newspaper and calling in to a talk radio or television show. Lee found that exposure to disagreement in discussions has a positive relationship with non– position-taking activities and a negative one with position-taking activities.

Lee (2012) considers that exposure to disagreement in political conversation positively relates to non–position-taking participation by enhancing people’s understanding of different viewpoints and their ability to handle these arguments. In other words, disagreement positively relates with participation in activities, leaving room for expressing the complexity and ambivalence of thought. However, disagreement-induced ambivalence and complexities in opinion are likely to make people feel uneasy about taking up a fixed and inflexible position, which is why, according to Lee, these same conversations would negatively affect position-taking activities.

Extending Lee’s (2012) conceptualization, we argue that distinguishing between some participatory forms that are by nature episodic, which require a decision but are also acts that do not require a regular commitment, and iterative, which typically require ongoing action with the aim of approaching a desired goal, can contribute to reconcile findings within this fractured paradigm. In our conceptualization, iterative actions refer to activities such as volunteering, working in a community project, or participating in an extended dialogue over an issue that the person cares about. In these activities, understanding different points of views and being able to work around these differences are critical to achieve desired results. Episodic actions, on the other hand, refer to single instances of participation in which the person makes a decision to vote or not, or to vote for one candidate and not for another. In episodic activities, the information gained from differing points of view can make the decision more complex. We propose that participatory activities that have been conceptualized by previous literature as civic and expressive conform to a domain of iterative participation, whereas voting and protesting behaviors could be examples of episodic participation. We are aware that certain individuals may change the “nature” we are ascribing to these participatory behaviors. For example, a long-time union organizer might participate in a protest as part of a broader spectrum of activity; therefore, for this person, protesting could be more of an iterative form of participation. Nevertheless, we contend that for the majority of the population, the episodic–iterative distinction can be fruitful in understanding the effects of exposure to disagreement.

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feel less need to express their complexities and ambivalence by participating in iterative political activities, in part because the lack of disagreement in their network suggests to them there is no need to.

Yet, disagreement may increase the civic and expressive participation of people having frequent political conversations with dissimilar others because, in the long term, after having discussed an issue over time, they become more confident about their views, the importance of the issue at stake, and the fact that others disagree with their position. Thus, if action is not taken, the issue might be shaped by the “wrong” views. It is also possible that participants in civic and expressive activities may tolerate divergent viewpoints in politics because fixed positions are not required for participation (Lee, 2012). We believe that exposure to disagreement in political discussions should be positively related to iterative forms of participation. Therefore, we posited our second hypothesis:

H2: Exposure to political disagreement will be positively related to iterative forms of participation such as (H2a) civic and (H2b) expressive participation.

Lee (2012) hypothesized a negative relationship between discussion disagreement and protesting and voting participation, but his results only partially supported the hypotheses. There was a negative relationship between disagreement discussion and protesting, but no relationship with voting turnout. Whereas disagreement in political conversations might provide people with additional information and opportunities to participate, it is also plausible that cross-pressures might have a chilling effect over more episodic forms of participation that require an encapsulated commitment to act immediately. Therefore, we posed the following research question:

RQ1: Will exposure to political disagreement be related to episodic forms of participation such as voting (RQ1a) and protesting (RQ1b)?

As mentioned above, previous literature has established that larger discussion networks are positively associated with increased engagement. Furthermore, Kwak and colleagues (2005) convincingly demonstrated how structural characteristics of someone’s discussion network interact with features of the conversation itself to result in increased participation. In the same vein, it is also plausible that, in certain instances, the discussion network size may amplify the effects of disagreement on political participation. For iterative participation, it is possible that larger networks, in which more disagreement occurs, provide members with more opportunities to engage in discursive practices that let them rehearse how to defend their ideas. These practices would therefore result in more efficacious and informed positions. Yet, for episodic participation, it is not clear whether increases in knowledge might be countered by cross-pressures and ambivalence that diminish the relationship between disagreement and participation. To explore the possibility that network size and political discussion interaction, we posed a final research question:

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Study Context

To test our ideas, we selected Colombia, a country that has experienced political turmoil, but also one in which democratic institutions have been gaining ground. Thus, Colombia is an important scenario to examine the effects of political conversation on different forms of participation. Colombia’s political system can be characterized as that of a formal democracy in which regular elections are held. A traditional conservative–liberal party divide has evolved in recent years into a multiparty system with multiple parties representing the right (which support, for example, free trade and a strong military, e.g., Partido de la U, Conservative Party), in the center (which seek social reforms, e.g., Partido Liberal, Partido Verde), and the left (which propose a wider role for government, the protection of Colombian production and land redistribution, e.g., Polo Democrático Alternativo). The press in Colombia tends to be closely tied to big business interests and can be described as a market‐based press with a “weak legacy of media pluralism” (Waisbord, 2008, p. 3).

A failed peace process with FARC, Colombia’s oldest and most important guerrilla group, influenced the presidential election in 2002. Then, Alvaro Uribe, a right-wing politician who promised that guerrillas would be defeated through the use of force, was elected president (and reelected for a second four-year term in 2006). While president, Uribe escalated the government offensive against leftist rebels and negotiated a peace process with paramilitary groups, sending some of its members to prison and causing others to reorganize themselves into emerging outlaw groups.

Despite Uribe’s popularity, various scandals involving corruption in government contracts, human rights violations, and illegal monitoring of opposition parties enhanced skepticism and distrust for those with differing views (Rodriguez & Seligson, 2008). This distrust can be characterized as a left–right divide, which has been intensifying. The number of people who identified with the center decreased in the first decade of 21st century and the number of people identifying with the extreme right grew (Rojas, Orozco, Gil de Zúñiga, & Wojcieszak, 2011).

In 2010, Uribe’s former defense minister and Partido de la U candidate, Juan Manuel Santos, was elected president, defeating Antanas Mockus from Partido Verde (Green Party). Despite the popularity of the governing party at that time, Partido Verde was successful using new media to mobilize independent voters and becoming a viable candidate, despite its ultimate loss in the second electoral round. Once elected, Santos distanced himself from Uribe, moving the Partido de la U closer to the center and initiating a peace process with FARC. Uribe and some members of Partido de la U then left this party to create a new coalition under the name Centro Democrático. In 2014, Santos was reelected for a second term in office, running against Oscar Ivan Zuluaga from Centro Democrático.

Method

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(Departamento Administrativo Nacional de Estadística, 2012). Survey respondents were selected using a multistep stratified random sample procedure that selected households randomly on the basis of city size and census data. Once the number of households was allocated for a given city, a number of city blocks were selected randomly according to housing district and strata. Then, individual households were randomly selected within each block. Finally, the study used the “adult in the household who most recently celebrated a birthday” technique to identify an individual respondent at random. Up to three visits to each household were made (if needed) to increase participation in the survey. A local professional polling firm, Deproyectos Limitada, collected the data and 1,031 face-to-face completed responses were obtained for a response rate of 83%.1

Key Variables

Participation. Respondents were asked whether, in the past 12 months, they had participated in any of 13 different political activities (no = 0, yes = 1). The list of activities included donating to a social or environmental group, donating to a church or charity, participating in volunteer work, working in a community project, attending an educational meeting, writing a letter to the editor of a newspaper, calling in to a live radio or television show, participating in a local municipal council, attending a political rally, attending a public meeting of their city, protesting by blocking a street, attending a social or political protest, voting in the last election for mayor, and voting in the last election for the city council. Using factor analysis, we established four different types of political activities that were classified as either iterative (i.e., civic and expressive) or episodic (i.e., voting and protesting).

A principal components analysis with oblimin rotation confirmed that responses to these questions correlated with each other in distinguishable ways (see Tables 1 and 2). Four components were identified, and these accounted for 58.33% of the original variance between them using Kaiser’s criterion. The first component, accounting for nearly 26.92% of the variance, was strongly related to civic participatory behaviors (Cronbach’s α = .74). The second component was voting participatory behaviors, which accounted for a further 11.80% of the variance (Cronbach’s α = .71). The third component accounted for approximately 10.53% of the variance and indicated protesting participatory behaviors (Cronbach’s α = .61). The fourth component was expressive participatory behaviors, accounting for approximately 9.09% of the variance (Cronbach’s α = .62).

Network size. Discussion network size was assessed by measuring the size of the discussion network within which individuals had political conversations. Respondents were asked to estimate the number of family members, friends or acquaintances, neighbors, and coworkers or classmates that they had talked politics with in the past month (minimum = 0, maximum = 246, M = 9.16, SD = 15.90). Because the result was positively skewed and the standard deviation was high, square root was applied to correct for skew (minimum = 0, maximum = 15.68, M = 2.44, SD = 1.79).

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Table 1. Structure Matrix for Political Participation with Oblimin Rotation of Factor Analysis.

Behavior Civic Voting Protesting Expressive

Participated in volunteer works .74 .01 .35 –.27

Donated to a social or environmental group .73 .05 .21 –.13

Donated to a church or charity .71 .09 –.02 –.16

Worked in a community project .67 .05 .30 –.36

Attended an educational meeting .64 .14 .15 –.38

Voted in the last election for mayor .05 .88 .03 –.03

Voted in the last election for the city council .09 .88 .09 –.10

Attended a social or political protest .30 .13 .78 –.26

Attended a political rally .14 .11 .72 –.40

Protested by blocking a street .17 –.01 .70 –.05

Wrote a letter to the editor of a newspaper .25 .04 .14 –.80 Called in to a live radio or television show .35 .09 .14 –.76

Participated in a local municipal council .19 .07 .31 –.66

Eigenvalue 3.50 1.53 1.37 1.18

Variance accounted for, % 26.92 11.80 10.53 9.09

Note. Extraction method: Principal component analysis. Rotation method: Oblimin with Kaiser normalization. Bold values represent items included in each factor.

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disagreement (M = 2.77, SD = 1.52, Cronbach’s α = .86). Although perceptual measures of diversity have been criticized, we argue that it is the perception of disagreement that matters. Another way of thinking about this is that real differences that are not perceived by the subject, due to for example processing strategies based on assimilation biases, would be inconsequential in terms of political mobilization. Although providing a definitive solution to problems surrounding the conceptualization and measurement of political disagreement goes beyond the scope of this article, we are confident that our measure of discussion disagreement captured this construct with validity based on the high correlations in previous research between different conceptualizations of disagreement and their similarities in predictive validity.

Control Variables

Demographics and control. Four demographic control variables were included in these models: gender (female n = 548, 53.2%; male n = 483, 46.8%), age (M = 41.12 years, SD = 16.21). The average education was high school completion (M = 5.12, SD = 1.54) on an 8-point scale (1 = none, 8 = graduate degree). Social status was assessed by Colombian system of national household energy level ranging from 1 to 6 (M = 2.91, SD = 1.09), and the higher values mean the bigger houses and more energy usage.

Political interest. In addition, we controlled for political interest and attention to political news as factors that previous research consistently has linked with different participatory behaviors. Political interest was assessed with three items on a 6-point scale that ranged from not at all to a lot. The items inquired for interest in (a) local politics, (b) national politics, and (c) international politics (M = 2.31, SD = 1.97, Cronbach’s α = .93).

Attention to political news. Respondents were asked how much attention they pay to national political news on 6-point scale ranging from not at all to a lot (M = 2.85, SD = 1.69).

Analytical Framework

Four linear regression analyses were conducted with different types of political participation variables. The models included demographic variables (gender, age, education, and social status) and control variables (political interest and political news attention) in the first block. Then, discussion network size was entered in the second block, exposure to disagreement in conversation in the third, and an interaction term for discussion network size by exposure to disagreement (variables centered to construct the interaction) in the fourth.

Results

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of incremental variance. The third block, exposure to disagreement, explained 0.8% of additional variance (β = .10, p < .01). The interaction between discussion network size and exposure to disagreement appeared as a significant predictor of civic participatory behaviors (β = .31, p < .01). This model supported H1, which predicted that network size would be positively related to participation, as well as H2a, which predicted that exposure to disagreement would be positively related to civic engagement.

The second regression model accounted for 9% of the variance of expressive participatory behaviors. Among the control variables, only political interest appeared to be a significant predictor (β = .11, p < .01), which explained 3.2% of the variance. Discussion network size was a significant predictor (β = .23, p < .001), explaining 4.6% of variance, and heterogeneous discussion was also a significant predictor (β = .09, p < .05), explaining 0.5% of the incremental variance. Lastly, there was a significant interaction between discussion network and exposure to disagreement (β = .24, p < .01), which explained 0.7% of the incremental variance. Thus, the model supported H1, which predicted a positive relationship between network size and participation, and H2b, which predicted a positive relationship between exposure to disagreement and expressive forms of engagement.

Table 2. Regression Models Predicting Civic,

Expressive, Voting, and Protesting Participatory Behaviors.

Iterative

participatory behaviors participatory behaviors Episodic

Variable Civic Expressive Voting Protesting

Block 1: Control

Gender (Female = 2) .01 –.03 .06* –.02

Age –.04 –.03 .20*** –.09**

Education .08* .05 .23*** –.00

Socioeconomic status –.05 .02 –.04 –.04

Political interest .20*** .11** .16*** .14**

Political news attention –.15*** .06 –.00 .10**

R2 (%) 4.6*** 3.2*** 10.2*** 4.7***

Block 2: Network size

Discussion network size .20*** .23*** .06 .13***

R2 (%) 8.1*** 7.8*** 10.5*** 6.1***

Block 3: Discussion

Exposure to disagreement .10** .09* –.06 .01

R2 (%) 8.9*** 8.3*** 10.8*** 6.1***

Block 4: Interaction Network Size 

Exposure to Disagreement

.31*** .24** –.12 .08

R2 (%) 10.0*** 9.0*** 10.9*** 6.2***

Note. Sample size = 1,031. Cell entries are standardized beta coefficients for Blocks 1, 2, 3, and 4.

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Two additional regression analyses were conducted to investigate the effects of exposure to disagreement on episodic forms of participation. The regression model predicting voting behaviors accounted for 10.9% of the variance. In this model, gender (β = .06, p < .05), age (β = .20, p < .001), education (β = .23, p < .001), and political interest (β = .16, p < .001) were significant predictors of voting, explaining 10.2% of the variance. However, neither discussion network size (β = .06, ns) nor exposure to disagreement (β = –.06, ns) was a significant predictor of voting behavior. This analysis also showed that the interaction was not a significant predictor of voting behavior (β = –.12, ns).

The last regression model predicting protesting participatory behaviors explained 4.7% of the variance. Among control variables, education (β = –.09, p < .01), political interest (β = .14, p < .01), and political news attention (β = .10, p < .01) were significantly related to the outcome and collectively explained 4.7% of the variance. Discussion network size was a significant predictor of protesting behaviors (β = .13, p < .001), providing additional support for H1. However, once again, both exposure to disagreement (β = .01, ns) and the interaction between network size and exposure to disagreement (β = .08, ns) were not significant. Thus, these regression analyses showed that there was no relationship between exposure to disagreement and the two episodic forms of participation considered.

Figure 1. Interaction of network size and civic participation.

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Figure 2. Interaction of network size and expressive participation.

Discussion

This study extends Lee’s (2012) categorization of participation in association with exposure to disagreement and discussion network size. We first extended Lee’s conceptualization of participation to classify specific behaviors as either iterative or episodic, and then we tested the relationships between these forms of participation and discussion network size, exposure to disagreement, and the interaction of the two.

As expected, we found a mobilizing effect of network size on three types of participatory behaviors, including civic, expressive, and protest participation. However, no relationship emerged with voting behavior. This latter result is consistent with previous studies in other contexts (e.g., Mutz, 2002). It is still not clear why network size would not have a positive effect on voting. It might be that social networks are less important for voting because of the influences of parties, political advertising, and strategic mobilization campaigns.

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positively influences participation when we consider forms that require repetitive acts with the aim of approaching desired goals, such as in volunteering and participating in civic communities.

However, our results are not consistent with Lee’s (2012) regarding the relationship between exposure to disagreement and episodic forms of participation such as voting and protesting. In our case, rather than a negative relationship, we found no relationship between exposure to disagreement and episodic participation. These differences might be context dependent. Nevertheless, our findings are consistent with other studies that also show no relationship (Huckfeldt, Johnson et al., 2004; Kloftsad et al., 2013; Nir, 2005).

Overall, our results contradict some previous literature that argues for the detrimental effect of exposure to disagreement on political participation. Instead, we found that disagreement had no relationship with certain forms of participation (episodic) and a positive relationship with others (iterative). Furthermore, we found that this relationship was amplified in larger social networks.

These results suggest that participation requiring ongoing conversation is enhanced by disagreement. Whether conversation in these cases operates as a consensus-achieving mechanism, serves a deliberative function, or enhances individual characteristics such as knowledge, certainty, or discomfort, is a question that future research needs to address. In this same vein, it will be important for future research to determine whether the nature of our findings is contextual (a limitation of this study), or whether similar results are obtained for episodic versus iterative forms of participation in other countries. It seems appropriate to inquire whether participation forms will align similarly in other contexts and whether apparently similar acts have the same meaning across contexts.

Our study design was cross-sectional in nature, and therefore cannot account for time and causal ordering. Longitudinal research would go a long way in providing support for the argument presented herein. It also remains to be seen if the intensity of participatory behavior could serve as an alternative explanation. It could be that issue-based activities are more iterative, and thus expressive and civic activities that tend to coalesce around issues are affected, whereas activity regarding an election, which involves a range of issues, is not.

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Finally, our study did not consider certain social–psychological variables that may also be involved in this process, such as tolerance for disagreement (Pattie & Johnston, 2008) and conflict avoidance (Ulbig & Funk, 1999), characteristics that may amplify or attenuate the impact of disagreement on political participation.

Ultimately, then, we need to explore the conditions under which disagreement does not enhance participation, which seems to be the underlying problem of democracy. If in certain contexts encountering disagreement becomes an obstacle for participation, one should ponder about the democratic nature of the participation being considered. To answer these fundamental questions, we need a comparative program of research.

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Figure

Table 1. Structure Matrix for Political Participation with Oblimin Rotation of Factor Analysis

Table 1.

Structure Matrix for Political Participation with Oblimin Rotation of Factor Analysis. View in document p.10
Table 2. Regression Models Predicting Civic,

Table 2.

Regression Models Predicting Civic . View in document p.12
Figure 1. Interaction of network size and civic participation.

Figure 1.

Interaction of network size and civic participation . View in document p.13
Figure 2. Interaction of network size and expressive participation.

Figure 2.

Interaction of network size and expressive participation . View in document p.14
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